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For: Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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1 Tampu IE, Haj-hosseini N, Blystad I, Eklund A. Deep learning for quantitative MRI brain tumor analysis.. [DOI: 10.1101/2023.03.21.23287514] [Reference Citation Analysis]
2 Khalid F, Goya-Outi J, Escobar T, Dangouloff-Ros V, Grigis A, Philippe C, Boddaert N, Grill J, Frouin V, Frouin F. Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities. Front Med (Lausanne) 2023;10:1071447. [PMID: 36910474 DOI: 10.3389/fmed.2023.1071447] [Reference Citation Analysis]
3 Wang J, Cai S, Zhang Z, Cai C. Editorial: Fast Multi-Parameter Magnetic Resonance Neuroimaging. Front Neurosci 2022;16:948993. [PMID: 35844219 DOI: 10.3389/fnins.2022.948993] [Reference Citation Analysis]
4 Ogier AC, Bustin A, Cochet H, Schwitter J, van Heeswijk RB. The Road Toward Reproducibility of Parametric Mapping of the Heart: A Technical Review. Front Cardiovasc Med 2022;9:876475. [DOI: 10.3389/fcvm.2022.876475] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
5 Gokyar S, Robb FJL, Kainz W, Chaudhari A, Winkler SA. MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate. IEEE Access 2021;9:140824-34. [PMID: 34722096 DOI: 10.1109/access.2021.3118290] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]